Assignment-01

 

Explain the difference between static and dynamic systems

Answer:

Static System:

A system whose response or output is due to present input alone is known as static system. The static system is also called the memoryless system. For a static or memoryless system, the output of the system at any instant of time (t for continuous-time system or n for discrete-time system) depends only on the input applied at that instant of time (t or n), but not on the past or future values of the input.

Dynamic System

A system whose response or output depends upon the past or future inputs in addition to the present input is called the dynamic system. The dynamic systems are also known as memory systems. Any continuous-time dynamic system can be described by a differential equation or any discrete-time dynamic system by a difference equation.

An electric circuit containing inductors and (or) capacitors is an example of dynamic system. Also, a summer or accumulator, a delay circuit, etc. are some examples of discrete-time dynamic systems.

Explain the difference between linear and nonlinear systems*

 

Linear System: A system is said to be linear if it obeys the principle of homogeneity and principle of superposition. Or A system may have input and output.A system is called Linear system

 if it satistfied  two conditions

(1)   If input is X=x1+x2 then the output must be Y=E[x1+x2]=E[x1]+E[x2]

(2)   If input is X=a.X1+b.X2 then the output must be Y=E[a.X1+b.X2]=aE[X1]+b.E[X2]               linear relationship exists between input and output

For example prices versus quality of petrol at filling station weight  applied to a string versus its extension

Non-linear A system is said to be a non-linear system if it does not obey the principle of homogeneity and principle of superposition.

Generally, if the equation describing the system contains square or higher order terms of input/output or product of input/output and its derivatives or a constant, the system will be a non-linear system. Triangulation of GPS signals is an example of non-linear system.

For Example Input and output have non-linear relationship fare versus distance traveled by air, land or sea

 

Find a linear system (write the mathematical model) that best explain the following set of input and output variables. Input (independent variable) = 3, 2, 4, 6    Output (dependent variable)= 4,5,9,10

Answer:

Independent variable=3,2,4,6

Dependent variable=4,5,9,10

Function =f(y)=mx+b

Where y=dependent variable

X=independent variable

M=stop

And can be calculated as: m=change in Y∕change in X

B=constant

Now calculating m and b

M=change in y∕change in x

M=1/1=0   as y is 4 and increase to 5 so change =1. 4 on this way change in x=-1

So y=mx+b

Put values 4=0(3)+b

4=0+b

B=4

Put value in functions

F(y)=mx+b

F(y)=0(3)+4

F(y)=4

So if we put 3 as input the function returns 4 as output

Now putting value 2 in function

M=4/2=2

Y=mx+b

5=2(2)+b

5=4+b

B=5-4=1

F(y)=mx+b

F(y)=2(2)+1

F(y)=4+1

5

And so on

 

Find the minimum and maximum value of the function f(x, y) = 2x - y. We are given the constraints:     5 ≥ y ≥ 2,                1 ≤ x ≤5,                y ≤ x + 3*

Answer:

Vertixes are

V(1)=(1,2)

V(2)=(2,5)

V(3)=(5,2)

V(4)=(5,5)

V(5)=(1,5)

Putting the value in function

(1)   f(1,2)=2(1)-(2)=0

(2)   f(2,5)=2(2)-(5)=-1

(3)   f(5,2)=2(5)-(2)=8

(4)   f(5,5)=2(5)-(5)=5

(5)   f(1,4)=2(1)-(4)=-2

 

So f(min)=-2

And f(max)=8

 

Explain the difference between Deterministic and   Stochastic models.*

Deterministic is that type of model in which we give specified input and it give us a model is able to provide expected solution

The best example is banking system

Stochastic is that of model in which we give a specified input and it give us a  model may produce  random output

The best example is radar system Model receiving signals corrupted by noise)

Unlike deterministic models that produce the same particular results for a particular set of inputs, stochastic models are the opposite; the model presents data and predicts outcomes that account for certain levels of unpredictability or randomness.

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